def load_data():
return getDataMatrix(TRAIN_FILES)
scores.append(score)
#train_score = np.sum(np.abs(clf.predict(X_scaled) - y)) / len(y)
train_score = np.sum(clf.predict(X_scaled) == y) / len(y)
train_scores.append(train_score)
lengths.append(len(y))
print("{:.4}\t\t{:.4}".format(train_score, score))
print( np.sum(np.array(scores) * np.array(lengths))/np.sum(lengths))
print(np.mean(scores))
"""
if __name__ == "__main__":
results = []
X, y = getDataMatrix(TRAIN_FILES)
scaler = preprocessing.Scaler()
scaler.fit(X)
for i, f in enumerate(TRAIN_FILES):
X, y = getDataMatrix(TRAIN_FILES[:i] + TRAIN_FILES[i+1:])
X_scaled = scaler.transform(X)
clf = linear_model.LogisticRegression(C=0.04)
clf.fit(X_scaled, y)
s1 = test(f, use_classified_shot=True)
s2 = test(f, use_classified_shot=False)
#s3 = test(f, use_classified_shot=False, use_history=False)
print(("{:.2%}\t\t"*2).format(s1, s2))
results.append((s1, s2))
r = np.array(results)
